Semantic Evolution over Populations for LLM-Guided Automated Program Repair

2026-04-02

AI Summary

This work addresses key limitations in existing large language model (LLM)-based automated program repair methods, particularly their shortcomings in repair diversity, semantic relationship modeling, partial repair composition, and utilization of failure feedback. To overcome these challenges, the paper proposes EvolRepair, a novel framework that introduces a semantic evolution mechanism into LLM-driven program repair for the first time. EvolRepair leverages behaviorally consistent repair populations, semantic-aware genetic operators, and structured execution feedback to enable family-based repair inference, cross-individual complementary synthesis, and dynamic search redirection guided by failure patterns. Experimental results demonstrate that EvolRepair significantly outperforms current LLM-based repair approaches in both repair success rate and solution quality.

Abstract

Large language models (LLMs) have recently shown strong potential for automated program repair (APR), particularly through iterative refinement that generates and improves candidate patches. However, state-of-the-art iterative refinement LLM-based APR approaches cannot fully address challenges, including maintaining useful diversity among repair hypotheses, identifying semantically related repair families, composing complementary partial fixes, exploiting structured failure information, and escaping structurally flawed search regions. In this paper, we propose a Population-Based Semantic Evolution framework for APR iterative refinement, called EvolRepair, that formulates LLM-based APR as a semantic evolutionary algorithm. EvolRepair reformulates the search paradigm of classic genetic algorithm for APR, but replaces its syntax-based operators with semantics-aware components powered by LLMs and structured execution feedback. Candidate repairs are organized into behaviorally coherent groups, enabling the algorithm to preserve diversity, reason over repair families, and synthesize stronger candidates by recombining complementary repair insights across the population. By leveraging structured failure patterns to guide search direction, EvolRepair can both refine promising repair strategies and shift toward alternative abstractions when necessary. Our experiments show that EvolRepair substantially improves repair effectiveness over existing LLM-based APR approaches.

Problem

• automated program repair
• semantic evolution
• repair diversity
• structured failure feedback
• repair families

Innovation

• Population-Based Semantic Evolution
• LLM-Guided Program Repair
• Semantic-Aware Operators
• Structured Failure Feedback
• Repair Family Recombination